May 9, Keshav Rao. Economics Honors Program Stanford University Stanford, CA Advisor: Jay Bhattacharya.

Transcription

1 Evaluating the Federal Food Assistance Safety Net: Do low-income individuals experience larger decreases in nutrient intakes during economic downturns than their wealthier counterparts? May 9, 2012 Keshav Rao Economics Honors Program Stanford University Stanford, CA Advisor: Jay Bhattacharya Abstract Low-income households often have less nutritious diets when compared to their wealthier counterparts due to decisions maximizing calories and minimizing costs. However, current research has not focused on whether gaps in nutritional intakes between different income groups have widened during periods of higher unemployment or whether food assistance programs protect the poor during these times. Using data from National Health and Nutrition Examination Survey and the Bureau of Labor Statistics ranging from 1999 to 2010, I conducted difference-in-difference tests to determine if low-income individuals experience a larger decrease in key nutrient levels and health indicators during economic downturns than richer counterparts. I found that higher periods of unemployment (proxy for worse economy) had mostly positive effects on adults living below 1.25 times the poverty level, as they increased total calories, fiber, vitamin C, vitamin E, magnesium, and potassium, while unfortunately elevating sugars, carbohydrates, and triglycerides. Interestingly, individuals above three times the poverty level had less protein, fiber, B vitamins, and other minerals during tougher economic periods, while the very wealthy tended to substitute towards unhealthier diets with more calories, fats, and sugars. Results for children also followed similar trends, as low-income groups improved key vitamin intakes while high-income groups moved towards more energy dense diets. A difference-in-difference-in-differences analysis showed that adult participation in the Women, Infants, and Children (WIC) program led to an increase in several key vitamins, cholesterol, and sodium; however, enrollment in the Supplemental Nutrition Assistance program (SNAP) was only shown to have indirect effects. With my preliminary results, I advocate an increase in WIC funding, a re-examination of SNAP, and a shift towards subsidizing more nutritious, healthy options. Keywords: Nutrition, low-income, food security, high unemployment, WIC, SNAP

2 Table of Contents Introduction 3 Literature Review...6 Data 14 Methodology..17 Results 21 Discussion..26 Figures 34 References..39 Appendix...43 Acknowledgements I would like to thank my advisor, Jay Bhattacharya, for all of his guidance and support throughout the research process, as well as my friends and family who have provided constant advice and encouragement over the past few quarters. 2

3 Introduction Much public debate following the 2008 recession has focused on societal inequalities and the efficacy of a growing number of federal welfare programs. Government assistance initiatives providing necessary services to individuals from the lowest socio-economic strata have recently been subjected to unprecedented strain from millions affected by high unemployment and the economic downturn. Nutrition is a notable case, as the Supplemental Nutrition Assistance Program (SNAP), the government s primary hunger relief program for low-income households, grew by 15.6 million participants since 2008, representing a 57% increase from prior levels. 1 The Women, Infants, and Children (WIC) program, another federal effort to supplement the diets of economically vulnerable subgroups, similarly observed near a million new participants and $1.5 billion in additional food costs since Recently, certain policymakers have advocated reducing funding for both programs, including a $134 billion cut over 10 years for SNAP and a $700 million cut for WIC in FY ,4 While data exists on the need for such programs to nutritionally help low-income families, little is known about their success in mitigating decreases in macronutrients and micronutrients for low-income individuals during difficult economic times, which is a key measure of efficacy. SNAP and WIC are designed to help individuals living near or below the poverty line that are at a higher risk for nutritional inadequacies and obesity based on decades of previous literature. The very poor fundamentally have stricter budget constraints than wealthier counterparts and rationally seek to maximize overall calories while minimizing costs. They have less money to spend on costly nutrient-dense items and struggle to find 3

4 healthy options that are reasonably priced in their neighborhoods. 5 Lower-income households therefore buy lower-quality meats, less seafood, and less fruits and vegetables (all typically more expensive food options) than US households in the top quintile by income. 6 A more recent USDA study also noted that low-income households spend $1.43 less per person on fruits and vegetables than their richer counterparts and do not change demand with corresponding increases in income, showing the relative importance of healthy eating to other necessities. 7 The different decision making criteria for low-income individuals leads to a diet dominated by energy-dense, nutrient-light foods filled with excessive fats, sugars, and refined grains, three key factors for obesity. 8,9 These individuals also often do not have adequate amounts of key macronutrients and micronutrients, as a recent study showed that only 72% of low-income enrollees in the Nebraska Nutrition Program had inadequate Vitamin A, C, protein, calcium, and iron quantities. 10 With such pronounced nutrient disparities, the bulk of the federal funding helps low-income families purchase fruits, vegetables, grains, meats, and/or dairy products, key staples for nutritionally balanced diets. However, over the past decade, while SNAP and WIC participation has rapidly increased with growing income inequalities, higher food insecurity, and periods of higher unemployment, there has not been a comprehensive evaluation of whether the programs protect low-income individuals from a widening nutrition gap by income during periods of economic downturns. The objective of the study is to determine if low-income individuals experience a larger decrease in key nutrient levels and health indicators than their richer counterparts after times of higher unemployment, a proxy for challenging economic times. A secondary objective is to determine whether SNAP and WIC serve as 4

5 adequate health and nutrition safety nets to help the poor maintain key nutrient levels during these difficult periods, an important factor in the funding debate. I conducted difference-in-difference tests of nutrient intakes for different economic subgroups to gauge whether intake gaps grew over time using data from the National Health and Nutrition Examination Survey (NHANES) and the Bureau of Labor Statistics. I also utilized a difference-in-difference-in-differences estimator to evaluate the nutritional impact of participation in federal assistance programs when comparing high unemployment periods to those with lower relative unemployment rates. By understanding how subgroups fare in terms of nutrient and vitamin levels during challenging economic times, policymakers will hopefully be able to improve the nutritional health of the poor and underserved. After finding that poor individuals improve dietary intakes of key nutrients in times of higher unemployment at the risk of increasing overall cholesterol and sodium, I would urge the government to introduce lowcost, nutritious options with reduced sugar, sodium, fats, and cholesterol for food assistance programs. Funding should be maintained and re-allocated towards healthier choices for WIC and SNAP participants to combat the prevalence of obesity, diabetes, and other diseases, with a clearer objective of improving the nutrition of low-income participants during economic downturns. On a separate note relating to overall healthcare policy, general nutrition education interventions should also be conducted with wealthier families who exhibit higher consumption of fats, sugars, and cholesterol during tougher periods, as energy dense foods remained cheap. 5

6 Literature Review Key Measures of Nutritional Adequacy Physicians, nutritionists, policy makers, and government officials have been concerned about the nutritional adequacy of diets for decades. Starting in 1968, daily nutrient intakes of individuals (often conducted through 24-hour dietary recalls) were compared to the FDA s recommended dietary allowances (RDA). The nutrient adequacy ratio (NAR) quantified the relationship between a sample and the recommended amount, while the mean adequacy ratio (MAR) averaged the sum of NARs over several nutritional values, effectively creating a baseline score factoring in key nutrients. Recently, RDAs have been replaced by dietary reference intakes (DRIs), which cover age-specific requirements (infants to those older than 70) for key vitamins, minerals, elements, macronutrients (carbohydrates, fibers, fats, fatty acids, proteins), electrolytes, and water. 11 Researchers have also supplemented the more traditional nutritional adequacy measurements with energy density calculations, healthy eating indices, and diversity/variety charts. The impetus behind the creation of these novel tools was the rising consumption of non-nutritious foods, which theoretically would force individuals to eat more energy-dense foods to reach suggested nutrition quotas. As Americans typically derive nearly 40% of energy from added sugars and fats, nutritionists thought it would be appropriate to examine whether individuals had higher quality diets defined by the higher intake of whole grains, lean meats, fresh fruits, vegetables, and lower added fats, sugars, and refined grains. 12 In fact, relative nutrient intakes are valuable in evaluating the impact of exogenous variables such as time and socioeconomic statuses. 6

7 Link Between Socioeconomic Status, Poor Nutrition, and Obesity Numerous publications have cemented the link between poverty, poor dietary nutrition, and obesity. First, individuals with limited financial resources often lack access to full-service grocery stores and farmers markets, which stock fresh fruits, vegetables, low-fat dairy products, and whole grains. 13, 14 The poor are thus forced to shop at local convenience or corner stores, which rarely have appealing fresh fruit and vegetable choices at affordable prices. 14,15 Instead, these stores carry a disproportionate amount of low-cost food with refined grains, added sugars, and added fats, essentially providing the basis for an energy-dense, nutrient-light diet. 16,17 The rational low-income individual seeks to maximize his/her caloric intake while minimizing costs and will consume less fish, poultry, fruits, and other healthier items, which are priced more expensively per calorie. 18 For example, Drewnowski found that a diet comprised of options with high caloric density cost $3.52 per day as opposed to $36.32 per day for a diet with low calorie density. The gap in pricing only worsened with inflation over the two-year study, as energy-dense choices decreased in price by 1.8% while low-calorie options increased in price by 19.5%. Unsurprisingly, low-income shoppers looking to stretch their budgets skew towards energy-dense diets with excessive refined grains, fats, sugars, which are strong predictors for obesity, elevated fasting insulin levels, and the metabolic syndrome in adults. 19 National surveys regarding obesity further confirm the disproportionate impact on those from lower socio-economic strata, as BMI has often grown the fastest amongst those with low family incomes. 20 Apart from generally poor diet choices and higher risks for obesity, low-income groups also have several key nutritional deficiencies when compared with the 7

8 recommended daily allowances. An early study looking at this inequality was Emmons work in 1986, which found that 76 low-income families had less Vitamin B6, Vitamin D, Vitamin E, iron, calcium, magnesium, zinc, and panthothetic acid than the daily recommendations, both at the beginning and end of the month, showing the continuous nature of the problem. Interestingly, however, the diets of the poor families met the suggested values for protein, ascorbic acid, thiamin, niacin, riboflavin, vitamin B12, Vitamin A, and phosphorous. 21 On a similar note, the Women, Infants, and Children government assistance program (WIC) redesigned their food package during the 1970 s and 1980 s to help low-income families attain the proper amounts of proteins, calcium, iron, Vitamin A, and Vitamin C. 22 However, a more recent study conducted by researchers at the University of Nebraska Lincoln in 2011 found that 70% of 100 lowincome families enrolled in the Nebraska Nutrition Education Program reported that they did not meet the adequate amount of key nutrients such as calcium, iron, protein, Vitamin A, and Vitamin C based on a 24-hour dietary recall. 10 Koszewski, the lead investigator, suggested that an increased frequency of family meals, especially breakfast, could improve nutrient intake from milk, fruits, and even fruit juices. Other Variables Affecting Diet Quality and Overall Nutrition Although income has the most pronounced effect on diet quality, overall nutritional adequacy, and risk for obesity, several previous studies identify potential demographic covariates including race, education, and sex. Raffensperger s analysis of the Baltimore area Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) evaluated 2000 individuals between the ages of 30 and 64 for several NARs and MARs 8

9 using micronutrient indices. 23 African Americans had an overall lower average MAR scores than their white counterparts, while also having lower individuals NAR scores for thiamin, riboflavin, folate, B12, Vitamin A, Vitamin E, magnesium, copper, zinc, and calcium. Interestingly, however, they had a significantly higher amount of Vitamin C than whites. Other studies also corroborate these studies, as Kant reported that African- Americans often substitute Vitamin-C infused beverages for more potassium and calcium heavy dairy products. The strong positive relationship between education level and the MAR composite across both races during a regression analysis was another significant result, although education and income are closely correlated. 24 The Healthy Eating Index (HEI), a USDA measure of a total diet that is comprised of 10-components and 100 points, was also related to age, education, and sex. Half of the index determines how well a given diet matches the USDA Food Pyramid guide (consumption of grains, vegetables, fruits, dairy products, and meat), while the other half measures macronutrients such as fat, saturated fat, cholesterol, sodium, and dietary variety. Several studies noted that HEI scores were higher for women, older individuals, and the more educated. 25, 26 Interestingly, according to the Continuing Survey of Food Intakes of Individuals from , education had a stronger impact than income on improving dietary quality as measured by the HEI index. 27 Additionally, it is important to observe that African-Americans once again had noticeable lower dietary quality than their white counterparts, but HEI measures were not significantly lower when comparing Latinos and Asians to whites. Apart from demographic variables, food security, defined as consistent access to meals, is strongly related to poor nutrition and obesity. When individuals eat less or skip 9

10 meals to stay within their limited budgets, they tend to overeat when food becomes available, which causes metabolic imbalances and weight gain. 28 Furthermore, energydense, nutrient poor choices that are easily found in low-income neighborhoods often characterize the binge eating. 29 Women, especially low-income mothers, are prone to fluctuations in nutritious diets, as they sacrifice nutrients to feed their hungry children. 30 Another study using NHANES III data ( ) showed that food-insecure women, when compared to women from food-secure households, had a lower overall mean HEI score (58.8 vs. 62.7), as well as lower HEI component scores for fruit (2.2 vs. 3.4), vegetables (5.1 vs. 5.8), milk (5.2 vs. 5.6), and food variety (6.4 vs. 7.3). 31 Several studies have also focused on the impact of demographic variables and food insecurity on the diet quality and nutritional adequacy of children. In recent years, according to a trends analysis of NHANES data from , male children and adolescents had a significantly higher prevalence of obesity than their female counterparts. However, the same study noted that race continues to be overwhelmingly correlated with obesity and poor nutrition, as the probability of being obese is considerably higher for black males (27%), black females (99%), Mexican-American males (81%) and Mexican-American females (47%) than their Caucasian counterparts. 32 To this point, another study analyzed the food purchasing behavior of low-income African-American youth in Baltimore and discovered frequent weekly purchases of chips, candy, and soda at corner stores. 33 In addition to demographic covariates, food insecurity also negatively impacted the healthy eating of children. Casey and colleagues analyzed a national, cross-sectional dataset of nearly 6000 children and found that children in low-income, food-insufficient households had fewer calories, fewer 10

11 carbohydrates, and more cholesterol intake than children growing up in higher-income, food sufficient environments. The children from the lower socioeconomic strata also were more likely to be overweight, with notably less fruit consumption. 34 Global Dietary Gap between Low-Income and High-Income Households While low-income individuals struggle to maintain nutritious diets and reach certain recommended daily nutrient requirements, their wealthier counterparts are able to spend on more expensive, healthy foods to create balanced meals. Adam Drewnowski, an expert in nutrition policy, compiled an international literature review to determine the extent of the socio-economic gradient on diet quality. 35 He analyzed mostly European studies focusing on disparities in common food groups, micronutrients, fiber, energy, and macronutrients. Individuals from higher socio-economic strata typically consumed more whole grains, lean meat, fish, vegetables, and fresh fruit in terms of quantity and variety Additionally, the bulk of the existing literature supported the claim that fiber, Vitamin C, Vitamin E, folates, B-Carotene, Calcium, and Iron intake were all positively related to income and education, even across both genders Finally, associations between socio-economic status and levels of macronutrients (such as carbohydrates, proteins, and different types of fats) and overall energy intakes were inconsistent or not significant across multiple studies, which is certainly plausible given the validation of the energy-dense diet geared towards the poor

12 Role of Government Assistance Programs to Help Improve Nutrition The Federal Government has several programs designed to help improve the diet quality and nutrient intakes of low-income families. The flagship service, the Supplemental Nutrition Assistance Program (SNAP), has helped over 44 million individuals purchase necessary food in 2011 with the average monthly benefits per household estimated to be $ Interestingly, research has shown an inverse relationship with obesity and SNAP participation, but has not shown a conclusive tie between improved dietary choices and the program for low-income families. A recent study determined that SNAP participants in Massachusetts who had been a part of the program for more than six months had a significantly lower BMI than those who had recently signed up, while another publication found that food-insecure individuals over the age of 54 were less likely to be overweight than non-snap participants. 43,44 However, a report from the USDA discovered that the majority of low-income shoppers spend their limited food benefits on energy-dense items with added fats and sugars, which is also supported by lower-cost red meats and milk/yogurt accounting for the largest share of purchases (7.8% and 7.6%, respectively). 45 Another important government-sponsored program is the Women, Infants, and Children (WIC) initiative that helps nutritionally supplement the diets of low-income women (pregnant and postpartum), infants, and children (up to age 5) while also offering education programs with referrals to health care. According to a Center for Nutrition Policy and Promotion study using data from the CSFII, WIC participation has historically been linked to improved participant HEI scores due to increased consumption from the five main food groups. 46 A more recent study also confirms the positive, 12

13 preventative impact of the nutrition program, as Karnik and colleagues found that food insecurity is positively related to increased BMI for women not enrolled in the WIC program. 47 WIC has also had a proven, early-stage impact on children in terms of preventing obesity, as national WIC participants were less likely to be obese than lowincome counterparts not enrolled in the program. 48 While the link between federal assistance programs and improvements in adult micronutrient adequacy is somewhat inconclusive, there is strong evidence that SNAP, WIC, and other nutritious school/after-school meal programs have improved the nutritional adequacy and dietary variety of children. Cole and Fox found that children between the ages of one and four participating in WIC had more nutrient-dense diets with less calories coming from solid fats and added sugars when compared to similar nonparticipants. 48 Bruening s study focusing on assistance programs targeted at child-care centers found that children had higher intakes of milk and vegetables while consuming fewer servings of fats and sweets. 49 She also finds that federally-funded after-school food programs helped children achieve a higher intake of calcium, vitamin A, and folate on program days when compared to the control of non-program weekdays. Finally, students eating nutritious meals at school are more likely to eat fruits, vegetables, and milk while substituting away from unhealthier foods found in vending machines and student stores Gaps in Existing Literature Review There are several noticeable gaps in the existing literature review, which can hopefully be addressed by this study. First, there are no prior studies examining the 13

14 impact of macro-economic forces, such as economic and unemployment cycles, on nutritional adequacy of a nationally representative sample of households from across the socio-economic spectrum. Recently, a qualitative study of female shoppers from households hit by unemployment in a Midwestern city found that individuals were using multiple strategies to save money but still knew characteristics of healthy food. 53 While this study evaluates the psychology of shoppers during a recession, it does not address quantitative concerns about a growing nutrient gap between high and low income individuals during economic downturns. Additionally, the elasticity of macronutrient and micronutrient intakes in light of fluctuating unemployment rates over the last twelve years has not been evaluated, especially when considering subgroups by income when controlling for race, sex, BMI, education, and age. Finally, on a related note, there have not been any studies to identify which subgroup is most and least sensitive to spiking unemployment amidst an economic downturn. Data The primary source of data was the National Health and Nutrition Examination Survey (NHANES), a set of CDC studies designed to assess the health and nutrition status of adults and children in the United States (NHANES). Although the program has been running since the 1960 s, the NHANES transitioned into a continuous set of surveys with an emphasis on dietary nutrition (from both 24-hour recall and lab testing) starting in For the purpose of this study, I merged respondent demographic, dietary, laboratory, and health-related questionnaire data from six two-year cycles spanning from 14

15 1999 to Unfortunately, NHANES did not release the dietary dataset for 2010, but offered the laboratory findings. Each cycle had approximately 10,000 unique participants from various counties that constituted a nationally representative sample, as subgroups were assigned sample weights based on demographic trends. Dietary Variables NHANES set up mobile examination centers to ask participants for detailed dietary intake information over the 24-hour period before the interview (midnight to midnight). The lengthy interviews provided estimates of macronutrients and micronutrients. Key macronutrients: Total energy, protein, carbohydrates, total fat, total saturated fat, total monounsaturated fatty acids, total polyunsaturated fatty acids, cholesterol, fiber, and total sugars. Key micronutrients and minerals: Vitamin B1 (Thiamin), Vitamin B2 (Riboflavin), niacin, Vitamin B6, total folate, Vitamin B12, Vitamin C, calcium, phosphorous, magnesium, iron, zinc, copper, potassium, Vitamin E (alpha-tocopherol), Retinol, Vitamin A, alpha-carotene beta-carotene, lycopene, lutein, folic acid, and Vitamin K. Laboratory Variables Individuals also participated in blood and urine tests at the NHANES mobile examination centers. The program collected valuable individualized information on the nutrition and/or general health related variables ranging from necessary vitamins to blood 15

16 count values. Below is a detailed breakdown of all NHANES laboratory variables analyzed. Biochemistry Profile: Albumin, total calcium, total cholesterol, glucose, iron, LDH, phosphorous, billirubin, protein, triglycerides, creatinine, sodium, potassium, globulin, ALT, AST, GGT, alkaline phosphotase, blood urea nitrogen, uric acid Complete Blood Count: White blood cell count, lymphocyte (%), monocyte (%), segmented neutrophils (%), eosinophils (%), basophils (%), red cell count, hemoglobin, hemotacrit (%), mean cell values (volume, hemoglobin, MCHC), platelet count, mean platelet volume Vitamins & Carotenoids: Vitamin A, Vitamin B6, Vitamin B12, Vitamin C, and Vitamin E, gamma tocopherol, retinyl palmitate, retinyl stearate, plasma glucose, insulin, total cholesterol, triglycerides, a-carotene, trans-b-carotene, cis-b-carotene, lutein, lycopene, transferring receptor Other: C-reactive protein, total iron binding complex, ferritin, red blood cell folate, serum folate, total mercury, cotinine Urinary: Albumin, creatinine, and mercury contained in urine samples Poverty, Food Assistance, & Demographic Variables While there are several variables to determine a family s income, the family poverty income ratio, a ratio of family income to the poverty threshold, is best suited for the study. This measure can help normalize results from the twelve-year span to show how relative poverty affects nutrient intake, as an absolute measure of poverty would be affected by macroeconomic variables and cost of living adjustments. The study also 16

17 utilizes questionnaire responses regarding whether individuals have participated in federal food assistance initiatives including the Women, Infants, and Children (WIC) and Food Stamp programs over the prior year. The NHANES surveys also provide in-depth demographic information on participants, along with relevant height, weight, and BMI measures. Important control variables include an individual s age (ranging from 0 to 85), sex (male or female), race (Mexican-American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and other), education (ranging from middle school to college graduate), and body mass index, as all of these factors can potentially influence nutrient intake over time. BLS Unemployment The Bureau of Labor Statistics (BLS) provided national unemployment rates at the end of each two-year cycle (2000, 2002, 2004, 2006, 2008, and 2010). The organization averages national county unemployment rates obtained through monthly household surveys throughout the year. Unfortunately, I was not able to procure countyspecific data for NHANES participants and could not engage in a granular analysis linking unemployment at the county level to nutrient intake. Methodology Complex Survey Design and Sample Weights NHANES utilizes a complex survey design with sample weights assigned to individuals in each two-year survey cycle in order to obtain a nationally representative dataset. The general population is divided into strata by geography and minority 17

18 populations, which are further split to create primary sampling units (PSUs). These PSUs are then divided into segments, after which researchers randomly draw a sample of participating households. Through this methodical process, sampling weights account for non-response, over-sampling, post-stratification, and sampling error. For the study, I merged data from six cycles ( ). I had to rescale the sample weights so that survey population at the midpoint of the time period matched the appropriate sum of the weights (NHANES) and had the following formula: Twelve Year MEC Weight = 1/3 * (Four Year MEC Weight) for 2000 or 2002 Twelve Year MEC Weight = 1/6 * (Two Year MEC Weight) for 2004, 2006, 2008, or 2010 Difference-in-difference Model A difference-in-difference estimator was the ideal choice to determine whether low-income individuals had significantly different nutrient intakes during higher periods of unemployment when compared to their wealthier counterparts. 54 The test evaluates effects on two unique groups over two time periods. The first group is exposed to the treatment effect only in the later time period, while the second group serves as the control (no treatment in either period). Therefore, the objective is to see if the treatment effect between the two periods for the first group is different from the difference in the control group over time. A model of the regression form is: y = β0 + β1db + δ0d2 + δ1d2 db + u where y represents the dependent variable of nutrient intakes. The db term is a dummy variable for low income, while d2 is a dummy variable for high unemployment (seen as another time period). Finally, the fourth term shows the interaction between low income 18

19 and high unemployment. In fact, the mean co-efficient on the interaction term, δ1, is the difference-in-difference estimator, where B is the treatment group (low income group) and A is the control (other wealthier economic subgroups): δ1 = (y B,2 y B,1) (y A,2 y A,1) For the study, I had to create two time periods based on high (>5.25) and low national unemployment rates (<5.25). Three years were classified as times of relatively high unemployment (2006, 2008, 2010), while three years were also characterized as times of low unemployment (2000, 2002, 2004). I also created five subgroups according to family income poverty ratio: x <= 1.25, 1.25 < x <= 2.5, 2.5 < x <= 3.75, 3.75 < x <= 5, x > 5. For the baseline case, my target treatment group was below the poverty line, but this was changed to test discrepancies in nutritional intake for the four other economic subgroups during tougher economic periods (high unemployment). While conducting the above difference-in-difference regression, I first needed to create two larger subgroups for adults (>=18 years old) and children (<18 years old), running each regression model separately due to inherent nutritional intake differences. Furthermore, I had to control for age, gender, race, education level, and BMI, which are all confounding variables that often affect nutrient measures. Difference-in-difference-in-differences Model I used a difference-in-difference-in differences model to analyze the effects of participation in federal food assistance programs (WIC and SNAP) and general food security status for low-income individuals during times of high unemployment. 54 Essentially, within the treatment state (low income), I created another treatment 19

20 (WIC/Food Stamp participation or food insecure status dummy variable) and control group (no federal food assistance program or food secure). The resulting model is: y = β0 + β1db + β2de + β3db de + δ0d2 + δ1d2 db + δ2d2 de + δ3d2 db de + u As in the difference-in-difference model, the db term represents low income and the d2 represents periods of high unemployment. However, there is also the de term, signaling either the WIC/food stamp participation or food insecure classification. The relevant coefficient is now the mean of the triple interaction term, δ3, which is: δ3 = (y B,E,2 y B,E,1) (y A,E,2 y A,E,1) (y B,N,2 y B,N,1) In the WIC example, this would start with nutrient intake changes for low- income, WIC participants over time before subtracting the change in means for non- poor WIC participants and the change in means for poor, non- WIC participants in the treatment state. This model actual controls for changes in nutrient levels for WIC participants that are due to other factors than family income while also controlling for the change in nutrient levels of all low- income families. For the study, I created a binary variable for WIC and SNAP/food stamp participation based on survey responses. I also defined food secure as household full security or marginal food security based on a series of responses to hunger, food availability, and affordability questions. The food insecure group was then defined as respondents with low or very low household food security, according to the same group of questions. 20

21 Bonferroni correction I used the Bonferroni correction to control for the probability of false positives from multiple hypotheses testing after regressing 105 total dietary and laboratory variables in various difference- in- difference models. Essentially, each hypothesis was tested at a significance level of 1/105 times the normal α = 0.05 to maintain a familywise error rate while running multiple comparisons. Results Adult Data Although none of the variables were significant using the very conservative Bonferroni correction, several adult dietary and laboratory variables were significant at the standard α = 0.05 level or lower, yielding interesting results as seen in Figures 1-3. Individuals in Group 1 (below 1.25 times the poverty line) showed generally higher intakes of key vitamins and minerals during higher periods of unemployment, but still reflected a shift towards energy-dense foods. Specifically, in the dietary surveys, simply being in this economic category had positive effects on calories (90.4 kcal), fiber (0.96 gm), total vitamin C (9.3 mg), magnesium (12.7 mg), potassium (145.9 mg), and vitamin E (1.1 mg), but also led to more sugar (10.6 gm) and carbohydrates (14.5 gm) for the low-income group. Laboratory results were also mixed, showing more vitamin E (50.8 ug/dl) and less serum glucose (-3.0 mg/dl), with more triglycerides (14.2 mg/dl) and less iron (-10.5 ug/dl). Subgroup 2 (family incomes between 1.25 and 2.5 times the poverty line) had no significant dietary changes between the two time periods but had mostly positive 21

22 differences on a few nutrition and health laboratory tests. Members of this group, who often miss federal assistance programs, reported higher protein (0.05 g/dl), vitamin D (1.7 mg/ml), and beta-carotene (2.2 ug/dl), with also less alcohol intake as measured through GTC (-24.4 ug/dl). However, the increase in LDL cholesterol (3.77 mg/dl) was the one main negative. Perhaps the most interesting group was Subgroup 3 (family incomes between 2.5 and 3.75 times the poverty line), as this effectively served as a negative control to Subgroup 1. Key results indicated mostly negative dietary results with a few contradictions from lab testing. In terms of dietary intake, individuals had less protein (-3.9 gm), fiber (-1.1 gm), vitamin B2 (-0.1 mg), folate (-26.3 mcg), vitamin B12 (-0.5 mcg), phosphorous (-75.1 mg), iron (-0.9 mg), zinc (-1.0 mg), potassium ( mg), and total folic acid (-22.2 mcg) in periods of higher unemployment. A few positive shifts did exist, including lower total fats (-4.8 gm), monounsaturated fats (-2.0 gm), and sodium ( gm). The lab results were more mixed, as individuals had more blood urea nitrogen (0.4), less serum glucose (3.4), and less total protein (-0.04 g/dl), but also had less alkaline phosphotase (-2.9 U/L), more vitamin A (1.9 ug/dl), more red blood cell folate (16.4 ng/ml RBC), and more phosphorous (0.05 mg/dl). Subgroup 4 (family income more than 3.75 and less than 5 times poverty level) did not exhibit much change between the two time periods, with the exception of slight substitution away from vitamin-filled foods. Positive results included less sugar (-6.9 gm) and more albumin (0.04 g/dl), while individuals had less Vitamin E (-1.2 mg) through dietary intake and less Vitamin A (-1.9 ug/dl) and D (-1.6 ng/dl) in the blood tests with higher total cholesterol (3.8 mg/dl). Meanwhile, Subgroup 5 (family income greater than 22

23 5 times the poverty level), showed problems with the diet and nutrition of the extremely wealthy during times of higher unemployment. These individuals tended to substitute towards more fatty, energy dense foods with a positive group effect for total calories (167.3 kcal), carbohydrates (20.7 gm), total fats (6.1 gm), saturated fat (2 gm), monounsaturated fat (2.9 gm), and sugar (16.3 gm). However, the wealthy did have less rst (-0.1 ug/dl) and vitamin E (-2.3 ug/dl) in their diets, and more creatine (0.05 mg/dl) and less total cholesterol (-6.1 mg/dl) in their blood tests. Effects of Federal Assistance Programs & Food Insecurity The difference-in-difference-in-differences (DDD) model, when controlling for age, race, gender, and education, showed that the effect of being low-income and participating in WIC was fairly positive in terms of vitamins and nutrients but led to higher sodium and cholesterol. Individuals had higher vitamin B12 (2.1 mcg) and zinc (2.0 mg), but also had more cholesterol (79.4 mg) and sodium (520.2 mg). Food stamp participation for this group did not have any effect during worse economic times with the surprising exception of lowering sodium intake ( mg). Another DDD model, with the additional control of BMI, showed an increase in vitamin K intake (34.5 mcg) paired with a spike in total cholesterol (116.5 mg), which supports the previous results as well. I also wanted to examine the impact of economic downturns on the food insecure subgroup. A similar DDD model showed that low-income individuals who are food insecure only saw positive nutritional gains during periods of higher unemployment. The most marginalized group saw an increase in the dietary intake of total calories (208.4 kcal), protein (9.9 gm), carbohydrates (34.3 gm), fiber (2.1 gm), vitamin B1 (0.3 mg), 23

24 vitamin B2 (0.3 mg), niacin (4.1 mg), vitamin B6 (0.4 mg), total folate (86.8 mcg), vitamin C (26.0 mg), phosphorous (149.3 mg), magnesium (36.3 mg), and total folic acid (63.8 mcg). It is important to note that there was no significant difference in the amounts of cholesterol, fats, or sodium over the two time periods. Children s Data I also separately analyzed dietary and laboratory variables for the five incomebased subgroups of children under the age of 18. While no variables were significant using the Bonferroni correction, I found several statistically significant variables at the standard α = 0.05 level as shown in Figures 4-6. Individuals in Subgroup 1 showed generally higher dietary intake of vitamins and minerals but still had elevated cholesterol intake in times of higher unemployment. Specifically, in the dietary surveys, the lowincome classification had positive effects on calcium (75.9 mg), protein (3.3 mg), vitamin B2 (0.2 mg), vitamin B6 (0.15 mg), vitamin B12 (0.5 mcg), phosphorous (78.3 mg), magnesium (15.5 mg), iron (0.8 mg), zinc (1.0 mg), potassium (157.8 mg), vitamin E (0.8 mg), serum glucose (2.3 mg/dl), vitamin D (3.2 ng/ml), and urinary creatine (8.4 mg/dl). Surprisingly, higher cholesterol (17.4 mg/dl) and lower potassium (-0.1 mmol/l) were the only negatives. Subgroup 2 showed a small set of mixed but mostly positive results, moving from periods of low to high unemployment. Individuals had a higher dietary vitamin E intake (0.8 mg), a lower blood urea nitrogen (-0.8 mg/dl), and more urinary creatine (8.7 mg/dl); however, there was also less retinyl palmitate (-0.3 ug/dl), less retinyl stearate (0.3 ug/dl), and higher LDL cholesterol (5.0 mg/dl), much like the adult cohort. 24

25 Subgroup 3, meanwhile, had a few negative effects for both dietary and laboratory tests but was not nearly as bad as the corresponding adult group. These children had lower phosphorous (-51.7 mg), zinc (-0.7 mg), copper (-0.1 mg), and vitamin D (-1.6 ng/ml) intakes. The only positive note was a decrease in LDL (-9.4 mg/dl) over the two time periods for the specific income classification. Interestingly, children from wealthy households, represented by Subgroups 4 and 5, saw more negative dietary and nutrition effects during periods of higher unemployment. Children within Subgroup 4 had significantly less vitamin B6 (-0.1 mg), vitamin E (-1.4 mg), beta-carotene ( mcg), vitamin D (-1.8 ng/ml), urinary creatine (-11.3 mg/ml), but had an elevated blood urea nitrogen level (0.9 mg/dl). Slightly lower sodium (-0.4 mmol/l) was the only beneficial dietary effect for the group over the two time periods. Children from Subgroup 5, meanwhile, saw higher total cholesterol (6.1 mg/dl) and triglyceride levels (22.9 mg/dl), which were not outweighed by small increases in globulin (2.4 g/dl) and lutein (1.7 ug/dl). Effects of Federal Assistance Programs & Food Insecurity The effects of federal assistance programs were almost entirely muted for lowincome children before and after periods of high unemployment. In fact, WIC participants from the lowest economic subgroup registered the only change: less insulin ( uu/ml). Food insecure children living below 1.25 times the poverty line also had almost negligible changes during times of spiking unemployment, with less reported vitamin B12 ( pg/dl) and more gamma tocopherol (40.1 ug/dl) from dietary recall and laboratory tests, respectively. 25

26 Discussion Adult Data The first major finding was that higher periods of unemployment had mostly positive nutritional effects on adults living below 1.25 times the poverty level when compared to periods of lower unemployment. I found that this group increased total calories, fiber, vitamin C, vitamin E, magnesium, and potassium, while unfortunately elevating sugars, carbohydrates, and triglycerides. The findings disproved my working hypothesis of a growing gap in key vitamins and nutrients for low-income individuals during tougher economic periods. In fact, the improvement in nutrient intake partly conflicts with the work of Drewnowski and Koszewski. Drewnowski showed that the poor maximize total calories within their limited budget constraints and often substitute away from poultry, fruits, and other healthy items, which are more expensive per calorie. 18 He found that fiber, vitamin C, vitamin E, folates, B-Carotene, Calcium, and Iron intake were all positively related to income and education, even across both genders. The Nebraska researchers similarly concluded that over 70% of low-income families sampled in a statewide program did not meet suggested amounts of calcium, iron, and several vitamins. 10 However, my study also shows the increase in total carbohydrates, sugars, and triglycerides for low-income individuals in tougher economic times, a conclusion supported by previous literature focusing on the shift towards higher energy density diets. Researchers have often cited the lack of low-fat, healthy options in low-income neighborhoods, but Drewnowski recently discovered a serious pricing differential between a high-caloric density diet ($3.52/day) and a healthier low-calorie diet 26

27 ($36.32/day), which only widens due to inflation. 18 Facing such prices, low-income consumers who have more constrained budgets during economically weaker time periods, would move towards low cost energy dense diets filled with refined grains, fats, and sugars. Food assistance program participation is one potential explanation for both higher vitamin and nutrient dietary intakes at the cost of rising negative indicators typical of unhealthy, processed foods. The WIC program was the only government initiative that had a strong positive impact on low-income individuals during times of higher unemployment, as SNAP was shown to have no significant effect on vitamins and minerals. The difference-in-difference-in-differences model, depending on controlled covariates, showed that WIC participants had higher vitamin B12, zinc, and vitamin K intakes but had elevated cholesterol and sodium. Although these results do not fully explain the range of dietary changes over the time periods, they do suggest a strong association. Furthermore, another regression model found that the food insecure, the most marginalized nutrition group, had only positive dietary effects (both macronutrients, key vitamins, and other micronutrients) during times of unemployment, indirectly showing the strength of welfare services. Finally, the analysis of Subgroup 3 (family incomes between 2.5 and 3.75 times the poverty line), which essentially served as a negative control, also supports the importance of societal nutritional safety nets. Individuals in this group, the middle class, are often disproportionately affected by spiking unemployment and do not qualify for food assistance programs. Based on the subgroup analysis, this group had mostly negative changes to dietary intake, ranging from less protein to less B vitamins. Therefore, the mix of positive direct and indirect evidence lends plausibility to 27

28 the argument that societal nutrition improvement programs are working during tough economic times; however, it remains difficult to isolate the direct impact (or lack thereof) for SNAP given that only a subset of participants were contained within the larger NHANES dataset. Previous literature also supports my findings regarding the generally positive impact of federal hunger prevention programs despite the skew towards energy-dense items. SNAP, which was formally known as the food stamp program, saw nearly a 60% increase in participation rates since the 2008 recession, with 90% of aid subsidizing the cost of fruits, vegetables, grains, meats, and dairy products for over 44 million individuals in Participation in the WIC program also improved HEI dietary scores from the five main food groups and maintained BMI for the food insecure. 46 However, several USDA reports observed that a large share of food stamp benefits went directly towards high-fat and high sugar items, especially as lower-cost red meats and milk/yogurt accounted for the two largest shares of purchases. 42,45 The second interesting result was that the very wealthy individuals substituted towards unhealthier diets with more fats, sugars, and calories during periods of higher unemployment. Subgroup 5 (family incomes greater than 5 times the poverty level) consumed more calories, carbohydrates, total fats, saturated fats, and sugars, but surprisingly had less total cholesterol through the blood test. However, the explanation for the dietary changes does not stem from unemployment, as the spending power of the top percentiles on food has not been constrained with the recent recession. These results instead reaffirm a national shift towards unhealthier foods since 2006, which was also the start of rising unemployment. Now, more than one in three American adults is obese, 28

29 with the epidemic driven partly by the rising consumption of added sugars and fats, staples which constitute a staggering 40% of daily energy sources. 12 The low prices of the high-fat, high sugar, energy dense items have also remained low relative to healthier, more organic options, making it easy for wealthy individuals to drastically increase their caloric intake while remaining around the same discretionary spending levels. Children s Data After analyzing nutritional changes for adults from various economic subgroups, I found similar trends for children in the NHANES dataset. First, children from the lowest income group fared better nutritionally during times of higher unemployment with more calcium, protein, B vitamins (B2, B6, B12), minerals (zinc, phosphorous, iron), vitamin D, vitamin E, and other micronutrients. However, there was still a negative: elevated cholesterol from presumably higher energy dense foods. Unfortunately, though, participation in federal food assistance programs could not explain these findings. A difference-in-difference-in-differences test showed only one negative effect, less insulin, for the WIC participation of low-income children. Additionally, food insecure children living in families with collective incomes less than 1.25 times the poverty line, had essentially negligible changes to macronutrient and micronutrient intakes during times of higher unemployment. Finally, even the negative control, Subgroup 3, was not very helpful. Children within that group only had a few negative results (less phosphorous, zinc, copper, and Vitamin D) but also had lower LDL, which provides an interesting but mostly ineffective comparison. 29

30 However, literature has confirmed the importance of federal hunger prevention programs for children. Several studies cite that WIC participation plays a key role in preventing low-income kids from become overweight or obese, while Cole and Fox found that children between the ages of 1 and 4 participating in WIC had more nutrientdense diet with less calories coming from solid fats and added sugars when compared to similar non-participants. 48 Therefore, there are two plausible explanations for the disconnect between my findings and some previous work in the field: small sample size and/or parents sacrificing food to feed children regardless of economic conditions. The first point reflects an experimental problem where the number of low-income children in WIC participating families (2125) was too low to generate significant results, as this was a very small subset of the larger NHANES population. Meanwhile, the second conclusion on the role of parental protection reflects a psychological phenomenon of low-income parents prioritizing the nutrition of children before themselves, which is a plausible, socially accepted norm shown in previous literature. 30 The last interesting result was that children from wealthy households had worse dietary effects during periods of higher unemployment. Children from Subgroup 4 (family incomes between 3.75 and 5 times the poverty level) simply had less vitamin intake (B6, E, and D), but Subgroup 5 (family income greater than 5 times the poverty level) saw higher total cholesterol and triglyceride levels. This again, is not reflective of the effects of unemployment or economic downturns, but rather further evidence of the shift towards unhealthy, processed, energy dense dietary choices for children since

31 Limitations and Further Work There are several limitations to the current study covering both experimental procedure and data. First, the NHANES study is not longitudinal and does not chart the nutrition information of the same individuals over the course of thirty years, which would give more accurate information on the impact of unemployment on nutritional adequacy. Also, I unfortunately could not get granular data on food purchasing patterns, county level NHANES nutrition information (restricted data), average county level prices for major food groups, and the environment of fast food locations/supermarkets/corner stores influencing consumption behavior. Another issue was that there was only a small subset of SNAP and WIC participants as a part of the larger sample, which could have contributed to the poor power of the difference-in-difference-in-differences test. A final limitation was that NHANES did not release the 2010 dietary data, which forced me to only incorporate laboratory test results. Moving forward, I plan to extend the study to find more granular relationships between unemployment and nutrient intakes for individuals across the socioeconomic spectrum. I will update the study with 2010 dietary information when the dataset is released and also request the restricted country level data to match with county specific unemployment rates. Finally, I would also try to obtain county level pricing data for major food groups to evaluate on a micro scale how local unemployment and food pricing impact nutrient intake. Hopefully, I will also be able to obtain more data on WIC and SNAP participants in the process. 31

32 Policy Implications Despite the limitations, the study still generated interesting preliminary results that could have potential policy implications. Based on the data, my first recommendation would be to increase the WIC funding, instead of cutting the budget by over $700 million. WIC is the most effective government food assistance program, as it improves key nutrient intakes including vitamin B12, zinc, and vitamin K for low-income pregnant and postpartum mothers during economic downturns. The return on investment is also often far greater than the single recipient, as improved nutrition for pregnant mothers leads to healthier children. However, I would advocate mandating healthier choices with low cholesterol and sodium or simply subsidizing these types of options, as the goal is to reduce the dependency on high fat, calorie dense, nutrient-light foods. My second recommendation would be to request a more detailed pre- and poststudy focused on the impact of SNAP participation on nutrient intake (dietary recall and lab results) during periods of high unemployment and other economic challenges. My difference-in-difference-in-differences test did not show any improvements for lowincome SNAP recipients during these challenging periods, but the positive result for the food insecure and the success of the negative control suggest that there might be indirect effects from the program or other societal food assistance initiatives. As it is too difficult to conclusively determine SNAP impact with the current experimental design, I would suggest retaining funding at current levels but would require a certain portion of the monthly benefits to be spent on healthy, inexpensive items with less sodium, fat, and cholesterol. If healthy items are too expensive, more funding should be allocated towards farmer s markets programs that subsidize the cost of nutritious fruits and vegetables. 32

33 However, if funding needs to be cut to satisfy the budget, I would urge Congress not to cut eligibility, but rather the amount of average benefits, as some have proposed a quick way to reduce participation by over eight million. The worse nutrient intakes observed for members of the negative control, lower middle class individuals who would not benefit from welfare assistance or other social programs in times of economic hardship, presents strong evidence against reducing the number of overall beneficiaries. General nutrition interventions should also be conducted for members of all socioeconomic groups, including the wealthier families who exhibited higher consumption of fats, sugars, and cholesterol in tougher economic periods. Key topics should include portion control, moderation of fats, the dangers of added sugars, and general healthy cooking tips. WIC and SNAP should also provide bilingual shopping and recipe guides designed to fit a range of family budgets and teach participants about healthy cooking and lifestyle choices through more workshops. Hopefully, these targeted initiatives across the socio-economic spectrum should combat the shift towards highdensity diets, which still remain very cost-effective during times of high unemployment. 33

34 Figures Figure 1: Subgroup Analysis of the Dietary Intake for Adults 34

35 Figure 2: Subgroup Analysis of Laboratory Tests for Adults (Part I) 35

36 Figure 3: Subgroup Analysis of Laboratory Tests for Adults (Part II) 36

37 Figure 4: Subgroup Analysis of the Dietary Intake for Children 37

38 Figure 5: Subgroup Analysis of Laboratory Tests for Children (Part I) Figure 6: Subgroup Analysis of Laboratory Tests for Children (Part II) 38

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43 Appendix of Regression Results Adults DID Income Subgroup 1 Adults DID Income Subgroup 2 43

44 Adults DID Income Subgroup 3 44

45 Adults DID Income Subgroup 4 Adults DID Income Group 5 45

46 Adults WIC DDD 46

47 Adults SNAP DDD Adults Food Insecure DDD 47

48 Children s DID Income Subgroup 1 Children s DID Income Subgroup 2 48

49 Children s DID Income Subgroup 3 Children s DID Income Subgroup 4 49

50 Children s DID Income Subgroup 5 Children s WIC DDD 50